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Underwater image enhancement method combining feature fusion and physical correction
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 11, Pages: 1554-1566(2023)
- 作者机构：
1.上海海洋大学 信息学院， 上海 201306
- 作者简介：
- 基金信息：Supported by National Natural Science Foundation of China(41776142)
DOI：10.37188/CJLCD.2022-0382
CLC：
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WANG De-xing, YANG Yu-rui, YUAN Hong-chun, et al. Underwater image enhancement method combining feature fusion and physical correction. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1554-1566(2023)
WANG De-xing, YANG Yu-rui, YUAN Hong-chun, et al. Underwater image enhancement method combining feature fusion and physical correction. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1554-1566(2023) DOI： 10.37188/CJLCD.2022-0382.
摘要
为解决由于光的吸收和散射现象导致拍摄的水下图像呈现出严重色偏，对比度低等质量问题，本文提出轻量级特征融合网络和多颜色模型校正相结合的水下图像增强方法。首先使用自构建块代替卷积层的编码器和解码器结构的特征融合网络对水下图像色偏进行校正，网络中改进的特征融合模块降低全连接层对图像空间结构的破坏，保护空间特征，减少模块的参数量。同时改进的注意力模块并行池化计算提取特征图纹理细节且保护背景信息。然后使用多颜色模型校正模块根据像素之间关系进行校正，进一步减少色偏，提高对比度和亮度。实验结果表明，与最新的图像增强方法对比，在有参考图像数据集上，本文方法的NRMSE、PSNR和SSIM评价指标的平均值分别比第二名提升了9.3%、3.7%和2.3%。在无参考图像数据集上，本文方法的UCIQE、IE和NIQE评价指标的平均值比第二名提升了6.0%、2.9%和4.5%。综合主观感知和客观评价，本文方法能校正水下图像色偏，提升对比度和亮度，提高图像质量。
Abstract
In order to solve the serious color bias and low contrast quality problems caused by light absorption and scattering， an underwater image enhancement method combining lightweight feature fusion network and multi-color model correction is proposed in this paper. Firstly， the feature fusion network of the encoder and decoder structure of the convolution layer is used to correct the color deviation of the underwater image. The improved feature fusion module in the network reduces the damage of the fully connected layer to the image spatial structure， protects the spatial features， and reduces the number of parameters of the module. At the same time， the improved attention module parallelizes the pooling operation to extract texture details and protect background information. Then， the multi-color model correction module is used to correct according to the relationship between pixels to further reduce the color deviation and improve the contrast and brightness.The experimental results show that compared with the latest image enhancement methods， the average value of NRMSE， PSNR and SSIM on the reference image dataset is improved by 9.30%， 3.70% and 2.30% than the second place of comparison algorithms， respectively. The average value of UCIQE， IE and NIQE on the non-reference image dataset is 6%， 2.9% and 4.5% higher than the second place of comparison algorithms. Combining subjective perception and objective evaluation， this method can correct color deviation of underwater images， improve contrast and brightness， and improve image quality.
关键词
图像处理神经网络注意力机制颜色模型编码解码结构
Keywords
image processingneural networksattention mechanismcolor modelencoding and decoding structure
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